import pygris

import geopandas as gpd
import numpy as np
import pandas as pd

import requests

import seaborn as sns
from matplotlib import pyplot as plt
import holoviews as hv
import hvplot.pandas

import cenpy

pd.options.display.max_columns = 999
pd.options.display.max_colwidth = None

available = cenpy.explorer.available()

acs = available.filter(regex="^ACS", axis=0)

available.filter(regex="^ACSDT5Y", axis=0)

cenpy.explorer.explain("ACSDT5Y2022")

D:\Mamba\envs\musa-550-fall-2023\lib\site-packages\libpysal\cg\alpha_shapes.py:39: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def nb_dist(x, y):
D:\Mamba\envs\musa-550-fall-2023\lib\site-packages\libpysal\cg\alpha_shapes.py:165: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def get_faces(triangle):
D:\Mamba\envs\musa-550-fall-2023\lib\site-packages\libpysal\cg\alpha_shapes.py:199: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def build_faces(faces, triangles_is, num_triangles, num_faces_single):
D:\Mamba\envs\musa-550-fall-2023\lib\site-packages\libpysal\cg\alpha_shapes.py:261: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def nb_mask_faces(mask, faces):

{'American Community Survey: 5-Year Estimates: Detailed Tables 5-Year': 'The American Community Survey (ACS) is an ongoing survey that provides data every year -- giving communities the current information they need to plan investments and services. The ACS covers a broad range of topics about social, economic, demographic, and housing characteristics of the U.S. population. Summary files include the following geographies: nation, all states (including DC and Puerto Rico), all metropolitan areas, all congressional districts, all counties, all places, and all tracts and block groups. Summary files contain the most detailed cross-tabulations, many of which are published down to block groups. The data are population and housing counts. There are over 64,000 variables in this dataset.'}

acs = cenpy.remote.APIConnection("ACSDT5Y2022")

len(acs.variables)

28193

housing_cost = acs.varslike(
    pattern="MEDIAN MONTHLY HOUSING COSTS",
    by="concept",  # searches along concept column
).sort_index()
housing_cost.head(1)

	label	concept	predicateType	group	limit	predicateOnly	hasGeoCollectionSupport	attributes	required

elderly = acs.varslike(
    pattern="Households with one or more people 60 years and over",
    by="label",  # searches along concept column
).sort_index()
elderly.head(1)

	label	concept	predicateType	group	limit	predicateOnly	hasGeoCollectionSupport	attributes	required
B11006_002E	Estimate!!Total:!!Households with one or more people 60 years and over:	Households by Presence of People 60 Years and Over by Household Type	int	B11006	0	NaN	NaN	B11006_002EA,B11006_002M,B11006_002MA	NaN

vacancy = acs.varslike(
    pattern="B25003",
    by="group",  # searches along concept column
).sort_index()
vacancy.head(3)

	label	concept	predicateType	group	limit	predicateOnly	hasGeoCollectionSupport	attributes	required
B25003A_001E	Estimate!!Total:	Tenure (White Alone Householder)	int	B25003A	0	NaN	NaN	B25003A_001EA,B25003A_001M,B25003A_001MA	NaN
B25003A_002E	Estimate!!Total:!!Owner occupied	Tenure (White Alone Householder)	int	B25003A	0	NaN	NaN	B25003A_002EA,B25003A_002M,B25003A_002MA	NaN
B25003A_003E	Estimate!!Total:!!Renter occupied	Tenure (White Alone Householder)	int	B25003A	0	NaN	NaN	B25003A_003EA,B25003A_003M,B25003A_003MA	NaN

ownership = acs.varslike(
    pattern="owner",
    by="label",  # searches along concept column
).sort_index()
ownership.head()

	label	concept	predicateType	group	limit	predicateOnly	hasGeoCollectionSupport	attributes	required
B07013PR_002E	Estimate!!Total:!!Householder lived in owner-occupied housing units	Geographical Mobility in the Past Year by Tenure for Current Residence in Puerto Rico	int	B07013PR	0	NaN	NaN	B07013PR_002EA,B07013PR_002M,B07013PR_002MA	NaN
B07013PR_005E	Estimate!!Total:!!Same house 1 year ago:!!Householder lived in owner-occupied housing units	Geographical Mobility in the Past Year by Tenure for Current Residence in Puerto Rico	int	B07013PR	0	NaN	NaN	B07013PR_005EA,B07013PR_005M,B07013PR_005MA	NaN
B07013PR_008E	Estimate!!Total:!!Moved within same municipio:!!Householder lived in owner-occupied housing units	Geographical Mobility in the Past Year by Tenure for Current Residence in Puerto Rico	int	B07013PR	0	NaN	NaN	B07013PR_008EA,B07013PR_008M,B07013PR_008MA	NaN
B07013PR_011E	Estimate!!Total:!!Moved from different municipio:!!Householder lived in owner-occupied housing units	Geographical Mobility in the Past Year by Tenure for Current Residence in Puerto Rico	int	B07013PR	0	NaN	NaN	B07013PR_011EA,B07013PR_011M,B07013PR_011MA	NaN
B07013PR_014E	Estimate!!Total:!!Moved from the United States:!!Householder lived in owner-occupied housing units	Geographical Mobility in the Past Year by Tenure for Current Residence in Puerto Rico	int	B07013PR	0	NaN	NaN	B07013PR_014EA,B07013PR_014M,B07013PR_014MA	NaN

population = acs.varslike(
    pattern="Population",
    by="concept",  # searches along concept column
).sort_index()
population.head(1)

	label	concept	predicateType	group	limit	predicateOnly	hasGeoCollectionSupport	attributes	required
B01003_001E	Estimate!!Total	Total Population	int	B01003	0	NaN	NaN	B01003_001EA,B01003_001M,B01003_001MA	NaN

poverty = acs.varslike(
    pattern="Percent!!YEAR STRUCTURE BUILT!!Total housing units!!Built 1939 or earlier",
    by="label",  # searches along concept column
).sort_index()
poverty.head(2)

	label	concept	predicateType	group	limit	predicateOnly	hasGeoCollectionSupport	attributes	required

variables = [
    "NAME",
    "B11006_002E", #Households with one or more people 60 years and over
    "B25002_003E", #Vacancy
    "B25003_003E", #Renter Occupation
    "B01003_001E", #Total Population
    "B19013_001E", #Median household income
    "B25035_001E", #Median year built
]

NYC_county_code = ["005","047","061","081","085"]
NY_state_code = "36"

county_codes = ",".join(NYC_county_code)

NYC_demo_data = acs.query(
    cols=variables,
    geo_unit="block group:*",
    geo_filter={"state": NY_state_code, "county": county_codes, "tract": "*"},
)

NYC_demo_data.head()

	NAME	B11006_002E	B25002_003E	B25003_003E	B01003_001E	B19013_001E	B25035_001E	state	county	tract	block group
0	Block Group 0; Census Tract 1; Bronx County; New York	0	0	0	0	-666666666	-666666666	36	005	000100	0
1	Block Group 1; Census Tract 1; Bronx County; New York	0	0	0	4446	-666666666	-666666666	36	005	000100	1
2	Block Group 0; Census Tract 2; Bronx County; New York	0	0	0	0	-666666666	-666666666	36	005	000200	0
3	Block Group 1; Census Tract 2; Bronx County; New York	230	15	99	1210	123208	1953	36	005	000200	1
4	Block Group 2; Census Tract 2; Bronx County; New York	302	31	379	2111	115764	1964	36	005	000200	2

NYC_block_groups = pygris.block_groups(
    state=NY_state_code, county=NYC_county_code, year=2022
)

NYC_demo_final = NYC_block_groups.merge(
    NYC_demo_data,
    left_on=["STATEFP", "COUNTYFP", "TRACTCE", "BLKGRPCE"],
    right_on=["state", "county", "tract", "block group"],
)
NYC_demo_final.head(1)

	STATEFP	COUNTYFP	TRACTCE	BLKGRPCE	GEOID	NAMELSAD	MTFCC	FUNCSTAT	ALAND	AWATER	INTPTLAT	INTPTLON	geometry	NAME	B11006_002E	B25002_003E	B25003_003E	B01003_001E	B19013_001E	B25035_001E	state	county	tract	block group
0	36	047	059403	1	360470594031	Block Group 1	G5030	S	64626	0	+40.5964380	-073.9474106	POLYGON ((-73.94905 40.59748, -73.94855 40.59753, -73.94812 40.59758, -73.94719 40.59768, -73.94623 40.59779, -73.94577 40.59540, -73.94674 40.59529, -73.94766 40.59519, -73.94859 40.59509, -73.94902 40.59735, -73.94905 40.59748))	Block Group 1; Census Tract 594.03; Kings County; New York	142	53	62	1185	125417	1938	36	047	059403	1

NYC_demo_final.rename(columns={
    "B11006_002E": "Households with 60+",
    "B25002_003E": "Vacancy",
    "B25003_003E": "Renter Occupation",
    "B01003_001E": "Total Population",
    "B19013_001E": "Median Household Income",
    "B25035_001E": "Median Year Built"
}, inplace=True)
NYC_demo_final['Households with 60+'] = pd.to_numeric(NYC_demo_final['Households with 60+'], errors='coerce').fillna(0).astype(float)
NYC_demo_final['Vacancy'] = pd.to_numeric(NYC_demo_final['Vacancy'], errors='coerce').fillna(0).astype(float)
NYC_demo_final['Renter Occupation'] = pd.to_numeric(NYC_demo_final['Renter Occupation'], errors='coerce').fillna(0).astype(float)
NYC_demo_final['Total Population'] = pd.to_numeric(NYC_demo_final['Total Population'], errors='coerce').fillna(0).astype(float)

NYC_demo_final['Median Household Income'] = pd.to_numeric(NYC_demo_final['Median Household Income'], errors='coerce').fillna(0).astype(float)
median1 = NYC_demo_final['Median Household Income'].median()
NYC_demo_final['Median Household Income'] = NYC_demo_final['Median Household Income'].mask(NYC_demo_final['Median Household Income'] < 0, median1)

NYC_demo_final['Median Year Built'] = pd.to_numeric(NYC_demo_final['Median Year Built'], errors='coerce').fillna(0).astype(float)
median2 = NYC_demo_final['Median Year Built'].median()
NYC_demo_final['Median Year Built'] = NYC_demo_final['Median Year Built'].mask(NYC_demo_final['Median Year Built'] < 0, median2)

NYC_demo_final.head(2)

	STATEFP	COUNTYFP	TRACTCE	BLKGRPCE	GEOID	NAMELSAD	MTFCC	FUNCSTAT	ALAND	AWATER	INTPTLAT	INTPTLON	geometry	NAME	Households with 60+	Vacancy	Renter Occupation	Total Population	Median Household Income	Median Year Built	state	county	tract	block group
0	36	047	059403	1	360470594031	Block Group 1	G5030	S	64626	0	+40.5964380	-073.9474106	POLYGON ((-73.94905 40.59748, -73.94855 40.59753, -73.94812 40.59758, -73.94719 40.59768, -73.94623 40.59779, -73.94577 40.59540, -73.94674 40.59529, -73.94766 40.59519, -73.94859 40.59509, -73.94902 40.59735, -73.94905 40.59748))	Block Group 1; Census Tract 594.03; Kings County; New York	142.0	53.0	62.0	1185.0	125417.0	1938.0	36	047	059403	1
1	36	047	059403	2	360470594032	Block Group 2	G5030	S	76076	0	+40.5941323	-073.9474463	POLYGON ((-73.94952 40.59499, -73.94859 40.59509, -73.94766 40.59519, -73.94674 40.59529, -73.94577 40.59540, -73.94537 40.59328, -73.94634 40.59317, -73.94726 40.59307, -73.94819 40.59297, -73.94912 40.59287, -73.94952 40.59499))	Block Group 2; Census Tract 594.03; Kings County; New York	120.0	77.0	106.0	800.0	88214.0	1938.0	36	047	059403	2

NYC_demo_final_Normalized = NYC_demo_final

NYC_demo_final_Normalized['Households with 60+'] = NYC_demo_final_Normalized['Households with 60+']/-1000
NYC_demo_final_Normalized['Vacancy'] = NYC_demo_final_Normalized['Vacancy']/500
NYC_demo_final_Normalized['Renter Occupation'] = NYC_demo_final_Normalized['Renter Occupation']/1500
NYC_demo_final_Normalized['Total Population'] = NYC_demo_final_Normalized['Total Population']/-6000
NYC_demo_final_Normalized['Median Household Income'] = NYC_demo_final_Normalized['Median Household Income']/-200000
NYC_demo_final_Normalized['Median Year Built'] = (NYC_demo_final_Normalized['Median Year Built'] - 1900)/122
NYC_demo_final_Normalized.head(1)

	STATEFP	COUNTYFP	TRACTCE	BLKGRPCE	GEOID	NAMELSAD	MTFCC	FUNCSTAT	ALAND	AWATER	INTPTLAT	INTPTLON	geometry	NAME	Households with 60+	Vacancy	Renter Occupation	Total Population	Median Household Income	Median Year Built	state	county	tract	block group
0	36	047	059403	1	360470594031	Block Group 1	G5030	S	64626	0	+40.5964380	-073.9474106	POLYGON ((-73.94905 40.59748, -73.94855 40.59753, -73.94812 40.59758, -73.94719 40.59768, -73.94623 40.59779, -73.94577 40.59540, -73.94674 40.59529, -73.94766 40.59519, -73.94859 40.59509, -73.94902 40.59735, -73.94905 40.59748))	Block Group 1; Census Tract 594.03; Kings County; New York	-0.142	0.106	0.041333	-0.1975	-0.627085	0.311475	36	047	059403	1

import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.font_manager as font_manager

NYC_demo_final_Normalized_Indexed = NYC_demo_final_Normalized
NYC_demo_final_Normalized_Indexed.index = NYC_demo_final_Normalized_Indexed['GEOID']
columns_to_keep = ["Households with 60+","Vacancy","Renter Occupation",
                                   "Total Population","Median Household Income","Median Year Built"]
new_df = NYC_demo_final_Normalized_Indexed[columns_to_keep]

k = -(1/np.log(new_df.shape[0]))

def entropy(X):
    return (X*np.log(X)).sum()*k

entropy = new_df.apply(entropy)

dod = 1 - entropy

w = dod/dod.sum()
w.sort_values(ascending = False)

D:\Mamba\envs\musa-550-fall-2023\lib\site-packages\pandas\core\arraylike.py:402: RuntimeWarning: divide by zero encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)
D:\Mamba\envs\musa-550-fall-2023\lib\site-packages\pandas\core\arraylike.py:402: RuntimeWarning: invalid value encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)

Median Year Built          0.461299
Renter Occupation          0.345718
Vacancy                    0.198211
Households with 60+       -0.001743
Total Population          -0.001743
Median Household Income   -0.001743
dtype: float64

NYC_demo_final_Normalized["Adaptivity"] = NYC_demo_final_Normalized['Vacancy']*0.198211
NYC_demo_final_Normalized.head()

	STATEFP	COUNTYFP	TRACTCE	BLKGRPCE	GEOID	NAMELSAD	MTFCC	FUNCSTAT	ALAND	AWATER	INTPTLAT	INTPTLON	geometry	NAME	Households with 60+	Vacancy	Renter Occupation	Total Population	Median Household Income	Median Year Built	state	county	tract	block group	Adaptivity
GEOID
360470594031	36	047	059403	1	360470594031	Block Group 1	G5030	S	64626	0	+40.5964380	-073.9474106	POLYGON ((-73.94905 40.59748, -73.94855 40.59753, -73.94812 40.59758, -73.94719 40.59768, -73.94623 40.59779, -73.94577 40.59540, -73.94674 40.59529, -73.94766 40.59519, -73.94859 40.59509, -73.94902 40.59735, -73.94905 40.59748))	Block Group 1; Census Tract 594.03; Kings County; New York	-0.142	0.106	0.041333	-0.197500	-0.627085	0.311475	36	047	059403	1	0.021010
360470594032	36	047	059403	2	360470594032	Block Group 2	G5030	S	76076	0	+40.5941323	-073.9474463	POLYGON ((-73.94952 40.59499, -73.94859 40.59509, -73.94766 40.59519, -73.94674 40.59529, -73.94577 40.59540, -73.94537 40.59328, -73.94634 40.59317, -73.94726 40.59307, -73.94819 40.59297, -73.94912 40.59287, -73.94952 40.59499))	Block Group 2; Census Tract 594.03; Kings County; New York	-0.120	0.154	0.070667	-0.133333	-0.441070	0.311475	36	047	059403	2	0.030524
360470594042	36	047	059404	2	360470594042	Block Group 2	G5030	S	75413	0	+40.5908487	-073.9477886	POLYGON ((-73.94912 40.59287, -73.94819 40.59297, -73.94726 40.59307, -73.94686 40.59095, -73.94646 40.58883, -73.94738 40.58873, -73.94831 40.58863, -73.94872 40.59075, -73.94912 40.59287))	Block Group 2; Census Tract 594.04; Kings County; New York	-0.185	0.098	0.114667	-0.290500	-0.709745	0.311475	36	047	059404	2	0.019425
360470594041	36	047	059404	1	360470594041	Block Group 1	G5030	S	96434	0	+40.5908731	-073.9455877	POLYGON ((-73.94726 40.59307, -73.94634 40.59317, -73.94537 40.59328, -73.94497 40.59116, -73.94401 40.59126, -73.94361 40.58915, -73.94457 40.58904, -73.94553 40.58893, -73.94646 40.58883, -73.94686 40.59095, -73.94726 40.59307))	Block Group 1; Census Tract 594.04; Kings County; New York	-0.261	0.000	0.108000	-0.220833	-0.245645	0.311475	36	047	059404	1	0.000000
360470594043	36	047	059404	3	360470594043	Block Group 3	G5030	S	42052	0	+40.5906922	-073.9492328	POLYGON ((-73.95015 40.59275, -73.94912 40.59287, -73.94872 40.59075, -73.94831 40.58863, -73.94934 40.58851, -73.94975 40.59063, -73.95015 40.59275))	Block Group 3; Census Tract 594.04; Kings County; New York	-0.317	0.000	0.441333	-0.236500	-0.248855	0.377049	36	047	059404	3	0.000000

NYC_demo_final_Normalized["Adaptivity"] = NYC_demo_final_Normalized["Adaptivity"] + 0.461299*NYC_demo_final_Normalized['Median Year Built']

NYC_demo_final_Normalized["Adaptivity"] = NYC_demo_final_Normalized["Adaptivity"] + 0.345718*NYC_demo_final_Normalized['Renter Occupation']

columns_to_keep_2 = ["GEOID","Adaptivity","geometry","NAME"]
NYC_Adaptive_Capacity = NYC_demo_final_Normalized[columns_to_keep_2]
NYC_Adaptive_Capacity.reset_index(inplace=True, drop=True)
NYC_Adaptive_Capacity.head()

	GEOID	Adaptivity	geometry	NAME
0	360470594031	0.178983	POLYGON ((-73.94905 40.59748, -73.94855 40.59753, -73.94812 40.59758, -73.94719 40.59768, -73.94623 40.59779, -73.94577 40.59540, -73.94674 40.59529, -73.94766 40.59519, -73.94859 40.59509, -73.94902 40.59735, -73.94905 40.59748))	Block Group 1; Census Tract 594.03; Kings County; New York
1	360470594032	0.198639	POLYGON ((-73.94952 40.59499, -73.94859 40.59509, -73.94766 40.59519, -73.94674 40.59529, -73.94577 40.59540, -73.94537 40.59328, -73.94634 40.59317, -73.94726 40.59307, -73.94819 40.59297, -73.94912 40.59287, -73.94952 40.59499))	Block Group 2; Census Tract 594.03; Kings County; New York
2	360470594042	0.202750	POLYGON ((-73.94912 40.59287, -73.94819 40.59297, -73.94726 40.59307, -73.94686 40.59095, -73.94646 40.58883, -73.94738 40.58873, -73.94831 40.58863, -73.94872 40.59075, -73.94912 40.59287))	Block Group 2; Census Tract 594.04; Kings County; New York
3	360470594041	0.181021	POLYGON ((-73.94726 40.59307, -73.94634 40.59317, -73.94537 40.59328, -73.94497 40.59116, -73.94401 40.59126, -73.94361 40.58915, -73.94457 40.58904, -73.94553 40.58893, -73.94646 40.58883, -73.94686 40.59095, -73.94726 40.59307))	Block Group 1; Census Tract 594.04; Kings County; New York
4	360470594043	0.326509	POLYGON ((-73.95015 40.59275, -73.94912 40.59287, -73.94872 40.59075, -73.94831 40.58863, -73.94934 40.58851, -73.94975 40.59063, -73.95015 40.59275))	Block Group 3; Census Tract 594.04; Kings County; New York

Criteria1_2 = pd.read_csv('./analysis/Data/Adaptive_And_Development.csv')
NYC_Adaptive_Capacity['GEOID'] = NYC_Adaptive_Capacity['GEOID'].astype(str)
NYC_Adaptive_Capacity['GEOID'] = NYC_Adaptive_Capacity['GEOID'].apply(lambda x: x[:-1])
Criteria1_2['GEOID'] = Criteria1_2['GEOID'].astype(str)

D:\Mamba\envs\musa-550-fall-2023\lib\site-packages\geopandas\geodataframe.py:1538: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  super().__setitem__(key, value)

Criteria1_2.head()

	OID_	GEOID	ALAND	AWATER	NAME	State	County	B01001_001E	B25002_001E	B25002_002E	B25002_003E	HOUSING_DENSITY	ADAPTIVE	DEVELOPMENT
0	1	36085000300	377461.0	2080565.0	Census Tract 3	New York	Richmond County	2187	1224	1087	137	3.242719	5.454462	18.831325
1	2	36085000600	666769.0	2045497.0	Census Tract 6	New York	Richmond County	2484	1439	1309	130	2.158169	10.511640	33.906883
2	3	36085000700	528114.0	605460.0	Census Tract 7	New York	Richmond County	4578	2203	1940	263	4.171448	3.337458	26.703180
3	4	36085000800	799850.0	0.0	Census Tract 8	New York	Richmond County	5751	2196	1926	270	2.745515	12.783682	30.304741
4	5	36085000900	231877.0	0.0	Census Tract 9	New York	Richmond County	1924	1006	740	266	4.338507	2.344675	30.374046

Adaptive_Capacity = NYC_Adaptive_Capacity.merge(Criteria1_2, on='GEOID')
max_value_1 = Adaptive_Capacity['ADAPTIVE'].max()
max_value_2 = Adaptive_Capacity['DEVELOPMENT'].max()
Adaptive_Capacity['ADAPTIVE'] = Adaptive_Capacity['ADAPTIVE']/ max_value_1
Adaptive_Capacity['DEVELOPMENT'] = Adaptive_Capacity['DEVELOPMENT']/ max_value_2

columns_to_keep_3 = ['ADAPTIVE','DEVELOPMENT',"Adaptivity"]
Final_TOPSIS = Adaptive_Capacity[columns_to_keep_3]

k = -(1/np.log(Final_TOPSIS.shape[0]))

def entropy(X):
    return (X*np.log(X)).sum()*k

entropy = Final_TOPSIS.apply(entropy)

dod = 1 - entropy

w = dod/dod.sum()
w.sort_values(ascending = False)

D:\Mamba\envs\musa-550-fall-2023\lib\site-packages\pandas\core\arraylike.py:402: RuntimeWarning: divide by zero encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)

Adaptivity     0.352945
ADAPTIVE       0.329026
DEVELOPMENT    0.318029
dtype: float64

columns_to_keep_4 = ["GEOID",'ADAPTIVE','DEVELOPMENT',"Adaptivity","geometry","NAME_x"]
Conclusion = Adaptive_Capacity[columns_to_keep_4]
Conclusion["TOPSIS_CALIBRATED"]=Conclusion["Adaptivity"]*0.352945 + Conclusion[
    "ADAPTIVE"]*0.329026 + Conclusion["DEVELOPMENT"]*0.318029

D:\Mamba\envs\musa-550-fall-2023\lib\site-packages\geopandas\geodataframe.py:1538: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  super().__setitem__(key, value)

Conclusion.fillna(0, inplace=True)

C:\Users\Josh Williamson\AppData\Local\Temp\ipykernel_15076\4250588969.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  Conclusion.fillna(0, inplace=True)

import altair as alt
from vega_datasets import data as vega_data
from sklearn.cluster import KMeans
kmeans = KMeans(n_clusters=5, n_init=10)
kmeans.fit(Conclusion[['ADAPTIVE','DEVELOPMENT','Adaptivity']])
Conclusion['label'] = kmeans.labels_
Conclusion.head()

D:\Mamba\envs\musa-550-fall-2023\lib\site-packages\geopandas\geodataframe.py:1538: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  super().__setitem__(key, value)

	GEOID	ADAPTIVE	DEVELOPMENT	Adaptivity	geometry	NAME_x	TOPSIS_CALIBRATED	label
0	36047059403	0.584123	0.539059	0.178983	POLYGON ((-73.94905 40.59748, -73.94855 40.59753, -73.94812 40.59758, -73.94719 40.59768, -73.94623 40.59779, -73.94577 40.59540, -73.94674 40.59529, -73.94766 40.59519, -73.94859 40.59509, -73.94902 40.59735, -73.94905 40.59748))	Block Group 1; Census Tract 594.03; Kings County; New York	0.426799	1
1	36047059403	0.584123	0.539059	0.198639	POLYGON ((-73.94952 40.59499, -73.94859 40.59509, -73.94766 40.59519, -73.94674 40.59529, -73.94577 40.59540, -73.94537 40.59328, -73.94634 40.59317, -73.94726 40.59307, -73.94819 40.59297, -73.94912 40.59287, -73.94952 40.59499))	Block Group 2; Census Tract 594.03; Kings County; New York	0.433737	1
2	36047059403	0.584123	0.539059	0.300804	POLYGON ((-73.95101 40.59726, -73.94997 40.59738, -73.94905 40.59748, -73.94902 40.59735, -73.94859 40.59509, -73.94952 40.59499, -73.94912 40.59287, -73.95015 40.59275, -73.95055 40.59487, -73.95088 40.59661, -73.95101 40.59726))	Block Group 3; Census Tract 594.03; Kings County; New York	0.469795	1
3	36047059404	0.487518	0.442388	0.202750	POLYGON ((-73.94912 40.59287, -73.94819 40.59297, -73.94726 40.59307, -73.94686 40.59095, -73.94646 40.58883, -73.94738 40.58873, -73.94831 40.58863, -73.94872 40.59075, -73.94912 40.59287))	Block Group 2; Census Tract 594.04; Kings County; New York	0.372658	1
4	36047059404	0.487518	0.442388	0.181021	POLYGON ((-73.94726 40.59307, -73.94634 40.59317, -73.94537 40.59328, -73.94497 40.59116, -73.94401 40.59126, -73.94361 40.58915, -73.94457 40.58904, -73.94553 40.58893, -73.94646 40.58883, -73.94686 40.59095, -73.94726 40.59307))	Block Group 1; Census Tract 594.04; Kings County; New York	0.364989	1

columns_to_keep_5 = ['ADAPTIVE','DEVELOPMENT',"Adaptivity","label"]
Conclusion_Chart = Conclusion[columns_to_keep_5]
Conclusion_Chart = Conclusion_Chart.head(5000)

	ADAPTIVE	DEVELOPMENT	Adaptivity	label
0	0.584123	0.539059	0.178983	1
1	0.584123	0.539059	0.198639	1
2	0.584123	0.539059	0.300804	1
3	0.487518	0.442388	0.202750	1
4	0.487518	0.442388	0.181021	1

(
    alt.Chart(Conclusion_Chart)
    .mark_circle()
    .encode(
        alt.X("ADAPTIVE", scale=alt.Scale(zero=False)),
        alt.Y("DEVELOPMENT", scale=alt.Scale(zero=False)),
        size="Adaptivity",
        color=alt.Color("label", scale=alt.Scale(scheme="viridis")),
        tooltip=list(Conclusion_Chart.columns),
    )
    .properties(width=400, height=300)
    .interactive()
)

Conclusion.plot(column='label', legend=True)
plt.xlabel('Longitude')
plt.ylabel('Latitude')
plt.show()

D:\Mamba\envs\musa-550-fall-2023\lib\site-packages\seaborn\axisgrid.py:1507: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
  func(x=vector, **plot_kwargs)
D:\Mamba\envs\musa-550-fall-2023\lib\site-packages\seaborn\axisgrid.py:1507: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
  func(x=vector, **plot_kwargs)
D:\Mamba\envs\musa-550-fall-2023\lib\site-packages\seaborn\axisgrid.py:1507: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
  func(x=vector, **plot_kwargs)
D:\Mamba\envs\musa-550-fall-2023\lib\site-packages\seaborn\axisgrid.py:1609: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
  func(x=x, y=y, **kwargs)
D:\Mamba\envs\musa-550-fall-2023\lib\site-packages\seaborn\axisgrid.py:1609: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
  func(x=x, y=y, **kwargs)
D:\Mamba\envs\musa-550-fall-2023\lib\site-packages\seaborn\axisgrid.py:1609: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
  func(x=x, y=y, **kwargs)
D:\Mamba\envs\musa-550-fall-2023\lib\site-packages\seaborn\axisgrid.py:1609: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
  func(x=x, y=y, **kwargs)
D:\Mamba\envs\musa-550-fall-2023\lib\site-packages\seaborn\axisgrid.py:1609: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
  func(x=x, y=y, **kwargs)
D:\Mamba\envs\musa-550-fall-2023\lib\site-packages\seaborn\axisgrid.py:1609: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
  func(x=x, y=y, **kwargs)
D:\Mamba\envs\musa-550-fall-2023\lib\site-packages\seaborn\axisgrid.py:118: UserWarning: The figure layout has changed to tight
  self._figure.tight_layout(*args, **kwargs)

Elderly_People = NYC_demo_final.to_crs(epsg=3857).hvplot(
    c="Households with 60+",
    frame_width=600,
    frame_height=600,
    alpha=0.7,
    geo=True,
    crs=3857,
    line_width=0.3,
    line_color='white',
    cmap="viridis",
    hover_cols=["Households with 60+"],
)
Elderly_People

import pandas as pd
from wordcloud import WordCloud
import matplotlib.pyplot as plt

df_1 = pd.read_csv('./Data/a.csv')

text = ' '.join(df_1['response'].dropna())

wordcloud = WordCloud(width=800, height=800, 
                      background_color='white', 
                      min_font_size=10).generate(text)
                       
plt.figure(figsize=(8, 8), facecolor=None) 
plt.imshow(wordcloud) 
plt.axis("off") 
plt.tight_layout(pad=0) 
  
plt.show()

ROW_D = gpd.read_file('./Shapefiles/Detention.shp')
ROW_D.head()

	assembly_d	asset_area	asset_id	asset_leng	asset_type	asset_widt	asset_x_co	asset_y_co	bbl	borough	city_counc	community_	construc_1	constructi	dep_cont_1	dep_contra	gi_feature	gi_id	nyc_waters	outfall	program_ar	project_na	row_onsite	secondary_	sewer_type	status	status_gro	tree_commo	tree_latin	geometry
0	52.0	1092.0	106022.0	0.0	Subsurface Detention System	0.0	9.887021e+05	187744.73807	3.003940e+09	Brooklyn	33.0	306.0	1	GI-CONS-1	1	OGI-DESIGN-1-OS7	NaN	WG-U2	Gowanus Canal	RH-034	Public Onsite	Wyckoff Gardens	Onsite	0.0	Combined	In Construction	In Construction	NaN	NaN	POINT (-73.98395 40.68199)
1	52.0	560.0	106044.0	0.0	Subsurface Detention System	0.0	9.869173e+05	188813.67431	3.003970e+09	Brooklyn	33.0	306.0	1	GI-CONS-1	1	OGI-DESIGN-1-OS1	NaN	BP-US1	Gowanus Canal	RH-034	Public Onsite	Boerum Park	Onsite	0.0	Combined	In Construction	In Construction	NaN	NaN	POINT (-73.99038 40.68493)
2	52.0	1388.0	106049.0	0.0	Subsurface Detention System	0.0	9.853902e+05	187550.45420	3.004420e+09	Brooklyn	39.0	306.0	1	GI-CONS-1	1	OGI-DESIGN-1-OS2	NaN	CP-US-BF	Gowanus Canal	RH-034	Public Onsite	Carroll Park	Onsite	0.0	Combined	In Construction	In Construction	NaN	NaN	POINT (-73.99589 40.68146)
3	28.0	504.0	106085.0	0.0	Subsurface Detention System	0.0	1.019317e+06	198028.13250	4.037960e+09	Queens	30.0	405.0	1	GI-CONS-1	3	OGI-DESIGN-1-OS6	NaN	MVP-US-2	Newtown Creek	NCB-083	Public Onsite	Middle Village Playground	Onsite	0.0	Combined	In Construction	In Construction	NaN	NaN	POINT (-73.87351 40.71015)
4	37.0	1773.0	106090.0	0.0	Subsurface Detention System	0.0	1.010978e+06	196031.50308	4.034800e+09	Queens	30.0	405.0	1	GI-CONS-1	3	OGI-DESIGN-1-OS9	NaN	RP-US-3	Newtown Creek	NCB-083	Public Onsite	Rosemarys Playground	Onsite	0.0	Combined	In Construction	In Construction	NaN	NaN	POINT (-73.90360 40.70470)

ROW_D.to_crs(epsg=3857).hvplot(
    frame_width=600,
    frame_height=600,
    geo=True,
    crs=3857,
    tiles='CartoLight',
    cmap="viridis",
    hover_cols=["asset_area"],
)